In the 1950s and 1960s, information retrieval (IR) theorists drew a distinction between“document retrieval systems” and “fact retrieval systems.” The former, were intendedto retrieve, in response to a user’s query, all documents that might contain informationpertinent to answering that query, while the latter were to lead the user directly tospecific pieces of information – facts – embedded within the documents being searchedthat would answer his or her question. The idea of information analysis clearlyprovided the theoretical impetus for fact retrieval (aka question-answering) systems

the emergence of the idea that documents could bedecomposed not only into smaller bibliographical units (as, for example, aperiodical into articles or a book into chapters), but also into yet smallerinformation units (such as, for example, the concepts or facts discussed indiscrete passages within a text) and that, once identified, these informationunits could be reconfigured in new arrangements that would facilitate theirretrieval [1, p. 223; 2, pp. 221–222].

Dr. Miho Ohsaki re-examined workshe and her group had previously published and confirmed that the results are indeed meaningless in the sensedescribed in this work (Ohsaki et al., 2002). She has subsequently been able to redefine the clustering subroutine inher work to allow more meaningful pattern discovery (Ohsaki et al., 2003)

Eamonn Keogh is an assistant professor of Computer Science at the University ofCalifornia, Riverside. His research interests are in Data Mining, Machine Learning andInformation Retrieval. Several of his papers have won best paper awards, includingpapers at SIGKDD and SIGMOD. Dr. Keogh is the recipient of a 5-year NSF CareerAward for “Efficient Discovery of Previously Unknown Patterns and Relationships inMassive Time Series Databases”.

In 1996 and 1998, a pair of workshops at the University of Glasgow on information retrieval and human–computer interaction sought to address the overlap between these two fields. Marchionini notes the impact of the World Wide Web and the sudden increase in information literacy – changes that were only embryonic in the late 1990s.

Warner's view is related to what might be termed a hermeneutical approach to searching (cf. Boell & Cecez-Kecmanovic, 2010) as opposed to a positivist approach. The positivist view implies that searching can be done in a formal way (algorithmic) that retrieves relevant knowledge without bias in the search (and this is the assumption behind evidence-based practice). The hermeneutic approach is based on the assumption that there is a constant reinterpretation of the relevant literature, implying the need for great flexibility in search criteria and a great level of iteration in search processes—and, most important, an understanding of what is going on during the search.

A fourth level of KO in Boolean systems is generated by the searcher

A third level of KO in classical databases consists of the information retrieval thesaurus,19 ontologies, and other kinds of controlled vocabulary constructed by information specialists.

Another level of KO is the bibliographical record and its organization into fields (and the corresponding organization of data in linear and inverted files). Such records vary from database to database and from host to host.

The selection of material to the bibliography constitutes the first and most basic level of KO. Because the meaning of terms is implicitly understood in this disciplinary context and to the extent that classification and indexing is based on the principle of “literary warrant,” this selection influences the developments of thesauri and ontologies, which may thus be understood as a higher level of KO.

Given the Boolean model, the goal for KO can be understood as improving bibliographical records in ways which improve searchers' selection power.

In the Boolean model, a great range of strategies are available to increase “recall” and “precision” (sometimes termed “recall devices” and “precision devices”). To utilize such devices in optimal ways, the user has to know about the databases, search facilities, documents, genres, languages, paradigms, and so on, in which he or she is searching. This should be part of what is often termed information literacy.

The problem is not that best-match systems are being developed, but that an ideological tendency to make things “user friendly” (and the market bigger) tends to hurt the development of systems aimed at increasing the selection power of users and search experts.

But much of the popularity of contemporary search engines may also be attributed to the easy pickings afforded by the first generation of Internet full-text based systems (owing to the cheap cost of digital storage capacity after 1990): no doubt it is good to have all text on the web indexed and made searchable—and often with free access. However, when the easy pickings have been utilized, more complex strategies (and more humanistic approaches) may be needed to make further progress.

Again, though, if maximum recall is required, it is impossible in ranked retrieval to know what is omitted by new queries, whereas Boolean queries allow the user to control and modify the search until a satisfactory result has been achieved and they therefore also seem better suited to iterative searches.

For a researcher conducting human studies, writing a dissertation, finding information pertinent to patient care, or conducting an in-depth literature review, Google Scholar does not appear to be a replacement for PubMed, though it may serve effectively as an adjunct resource to complement databases with more fully developed searching features.

To understand the possibilities of Boolean search when used in its most advanced ways, it is necessary to know about bibliographical records in online databases,

As previously mentioned, the medical domain is an exception to the general trend that the study of the optimization of document searching strategies has suffered in information science.